This article compares the performance of AMD and NVIDIA hardware when running Large Language Models (LLMs) as of May 2025, based on recent reports and trends.
Key Factors Influencing LLM Performance
VRAM (Video RAM)
The size of the GPU‘s memory is crucial for handling large LLMs. Larger models and higher precision require more VRAM. Insufficient VRAM can lead to slower performance due to data swapping.
Memory Bandwidth
The speed at which data can be transferred to and from the GPU’s memory significantly impacts LLM processing speed.
Compute Power (FLOPS)
The floating-point operations per second a GPU can perform directly affects the speed of calculations within the LLM.
Software Optimization
Libraries and frameworks like CUDA (NVIDIA) and ROCm (AMD), as well as inference engines (e.g., TensorRT-LLM, vLLM), are vital for efficient hardware utilization.
Interconnect (Multi-GPU)
For very large models on multiple GPUs, the speed and efficiency of the connection (e.g., NVLink) are important.
AMD vs. NVIDIA – Current Landscape (May 2025)
As of May 2025, NVIDIA maintains a strong position in the LLM space, especially for training and high-performance inference in data centers, largely due to its mature CUDA ecosystem.
- AMD’s Advancements: AMD’s newer professional GPUs (e.g., Radeon Pro W7900) with larger VRAM are showing competitive inference performance, particularly when VRAM is a bottleneck for NVIDIA.
- VRAM Advantage for AMD: Reports suggest AMD GPUs with larger VRAM can outperform some NVIDIA cards with less VRAM on very large LLMs that exceed NVIDIA’s VRAM capacity.
- Integrated Graphics Performance: AMD’s Ryzen AI Max+ CPUs (integrated graphics) have shown promising LLM performance on laptops, sometimes outperforming discrete NVIDIA GPUs in memory-intensive tasks.
- Software Ecosystem: NVIDIA’s CUDA ecosystem remains more mature and widely adopted, although AMD’s ROCm support is improving.
- Inference Engines: Both NVIDIA (TensorRT-LLM) and the open-source community (vLLM with AMD optimizations) are developing optimized inference engines.
- Multi-GPU Scaling: NVIDIA’s NVLink offers high-bandwidth interconnects for multi-GPU training. AMD’s multi-GPU adoption in LLMs is less prevalent.
- Price-to-Performance: AMD may offer a more competitive price-to-performance ratio in certain inference scenarios with high VRAM demands.
Key Takeaways from Recent Reports
- AMD’s Ryzen AI Max+ CPUs demonstrate strong LLM performance on laptops, sometimes surpassing discrete NVIDIA GPUs in memory-bound scenarios.
- AMD’s Radeon Pro W7800/7900 GPUs (48GB VRAM) have shown the ability to outperform NVIDIA’s RTX 4090 (24GB VRAM) in LLM inference with larger models.
- NVIDIA’s RTX 5090 (launched early 2025) offers improved LLM performance over the RTX 4090, but memory bandwidth can still be a factor.
- The CUDA ecosystem remains a significant advantage for NVIDIA in LLM development and research.
In Conclusion (as of May 2025)
NVIDIA currently leads in overall LLM performance, particularly for training and high-end inference, due to its software and hardware ecosystem. However, AMD is becoming increasingly competitive in inference, especially where large VRAM is critical, and shows strength in the mobile LLM space with its integrated graphics. The optimal choice depends on specific needs, budget, and software compatibility.
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